Data Science Lead

About the role

Title will be calibrated to experience. Pro members see the company and apply now. It opens to everyone on November 5.

What you'll do

• You will work closely with Product, Growth, Finance, Engineering, and AI to turn trusted data into better product and business decisions.
• Success means delivering measurable business improvements while building the systems and practices that let a small Data team support a growing company.
• Lead the team and strengthen the data foundation
• Set priorities, develop the team through technical direction and example, and focus capacity on the highest-impact problems. Personally lead ambiguous, high-stakes analyses.
• Own standards for metric definitions, data validation, and analytical quality. Partner with Engineering to diagnose and prevent recurring issues across tracking, pipelines, transformations, and dashboards.
• Turn recurring requests into reusable datasets, frameworks, and self-service tools so teams can make sound decisions with less manual support.
• Use behavioral analysis, user profiling, and segmentation to identify opportunities across activation, retention, monetization, and lifetime value. Translate findings into recommendations that inform product strategy,...
• Strengthen experimentation and causal measurement across Product and Growth. Evaluate acquisition quality and the long-term value of different channels and customer segments, moving beyond attribution toward...

What they're looking for

• Strong product and business judgment: you identify important questions, navigate ambiguity, and turn evidence into decisions.
• Strong SQL and Python skills and a willingness to stay hands-on.
• Deep experience with product metrics and user behavior, plus a strong foundation in statistics, A/B testing, and causal reasoning.
• Strong data-quality instincts and enough engineering knowledge to trace data through a system, diagnose recurring pipeline problems, and work effectively with engineers on durable fixes.
• Ability to lead through influence, technical credibility, and clear communication across technical and business teams.
• Deep data infrastructure expertise and model-training experience are not required.

Nice to have

• Experience measuring paid marketing incrementality and acquisition economics.
• Experience partnering with AI/ML teams on evaluation or data curation, or building AI-assisted or agentic analytics systems.
• Experience in SaaS, consumer software, creator products, subscription businesses, or AI products.